Environmental Scanning in Globally Oriented Small Businesses: Practices Suggested by Managers 1
Bibliographic record
Abstract
This paper identifies information sources and practices of environmental scanning preferred by managers of globally oriented small and medium-sized enterprises (GOSMEs). Data were collected using a Delphi technique and were analysed by NUD*IST software and the Homogeneity Analysis technique. Major findings indicate that although managers of GOSMEs generally prefer external and personal sources in their environment scanning process, contingent conditions related to the industry, the organization and the owner-manager guide the choice of appropriate information source and the need to scan systematically each sector of the environment. Statistical relationships were identified, and these relationships allowed the formulation of general propositions that could be helpful for practice and research in GOSMEs. The paper concludes that the manager's need to scan systematically a specific sector of the environment and the information source the firm might use are dependent on the level of uncertainty aroused by this sector, the amount of pertinent information the source has, and its accessibility by the firm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".